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Recently, diffusion-based image generation methods are credited for theirremarkable text-to-image generation capabilities, while still facing challengesin accurately generating multilingual scene text images. To tackle thisproblem, we propose Diff-Text, which is a training-free scene text generationframework for any language. Our model outputs a photo-realistic image given atext of any language along with a textual description of a scene. The modelleverages rendered sketch images as priors, thus arousing the potentialmultilingual-generation ability of the pre-trained Stable Diffusion. Based onthe observation from the influence of the cross-attention map on objectplacement in generated images, we propose a localized attention constraint intothe cross-attention layer to address the unreasonable positioning problem ofscene text. Additionally, we introduce contrastive image-level prompts tofurther refine the position of the textual region and achieve more accuratescene text generation. Experiments demonstrate that our method outperforms theexisting method in both the accuracy of text recognition and the naturalness offoreground-background blending.

Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model